A Technical Review of Convolutional Neural Network-Based Mammographic Breast Cancer Diagnosis

Abstract

This study reviews the technique of convolutional neural network (CNN) applied in a specific field of mammographic breast cancer diagnosis (MBCD). It aims to provide several clues on how to use CNN for related tasks. MBCD is a long-standing problem, and massive computer-aided diagnosis models have been proposed. The models of CNN-based MBCD can be broadly categorized into three groups. One is to design shallow or to modify existing models to decrease the time cost as well as the number of instances for training; another is to make the best use of a pretrained CNN by transfer learning and fine-tuning; the third is to take advantage of CNN models for feature extraction, and the differentiation of malignant lesions from benign ones is fulfilled by using machine learning classifiers. This study enrolls peer-reviewed journal publications and presents technical details and pros and cons of each model. Furthermore, the findings, challenges and limitations are summarized and some clues on the future work are also given. Conclusively, CNN-based MBCD is at its early stage, and there is still a long way ahead in achieving the ultimate goal of using deep learning tools to facilitate clinical practice. This review benefits scientific researchers, industrial engineers, and those who are devoted to intelligent cancer diagnosis.

A drug identification model developed using deep learning technologies: experience of a medical center in Taiwan

“…Most significantly, thanks to the vigorous development of Graphics Processing Units (GPUs) for parallel computing, a current mainstream process is to adopt deep learning methods to replace traditional classifiers. Examples include biomedical imaging and wave recognition [18,19]; speech recognition [20,21]; biomedical signal detection [18,19,22]; cancer identification [19,22,23]; potential drug discovery [24,25]; and adverse drug effects [26]. Images of the drug are pre-processed to obtain the correct viewing angle and drug separation, and the characteristics of the pills are established manually [27].…”

A Drug Identification Model developed using Deep Learning Technologies: Experience of a Medical Center in Taiwan

“…Most significantly, thanks to the vigorous development of Graphics Processing Units (GPUs) for parallel computing, a current mainstream process is to adopt deep learning methods to replace traditional classifiers. Examples include biomedical imaging and wave recognition [18,19]; speech recognition [20,21]; biomedical signal detection [18,19,22]; cancer identification [19,22,23]; potential drug discovery [24,25]; and adverse drug effects [26]. Images of the drug are preprocessed to obtain the correct viewing angle and drug separation, and the characteristics of the pills are established manually [27].…”

A Drug Identification Model developed using Deep Learning Technologies: Experience of a Medical Center in Taiwan

“…Most significantly, thanks to the vigorous development of Graphics Processing Units (GPUs) for parallel computing, a current mainstream process is to adopt deep learning methods to replace traditional classifiers. Examples include biomedical imaging and wave recognition [11,12]; speech recognition [13,14]; biomedical signal detection [11,12,15]; cancer identification [12,15,16]; potential drug discovery [17,18]; and adverse drug effects [19]. Images of the drug are preprocessed to obtain the correct viewing angle and drug separation, and the characteristics of the pills are established manually [20].…”